Season 2 / Episode 7

Analytics and Data

Season 2 / Episode 7

Analytics and Data

Featuring

Jennifer Shannon, Kevin Coombs

Date published

July 11, 2019

Tags

Analytics

Also available on:

In the final episode of Season 2, host Jennifer Shannon sits down with Rangle's Director of Data and Analytics, Kevin Coombs, to help break down the misconceptions and overall importance of data for your innovation strategy.

In the final episode of Season 2, host Jennifer Shannon sits down with Rangle's Director of Data and Analytics, Kevin Coombs, to help break down the misconceptions and overall importance of data for your innovation strategy.

Welcome to What's Your (R)angle? a podcast by Rangle that explores thoughts, ideas, and perspectives on the digital transformation space. Each episode we'll provide you with some key considerations for mapping your digital first strategy and our angle on how we look at challenges presented. Join the conversation by using the hashtag #askrangle on Twitter or by emailing us at podcast@rangle.io.

00:24

Hello and welcome to What's Your (R)angle? I am your host, Jennifer Shannon and this season on the podcast we're talking about the core components of digital transformation and innovation, and helping to demystify some of these components for folks. On today's episode, we're looking at the D word, data. And there have been a lot of conversations about data in the last few years, but today we're going to talk about the role of data as it pertains to your innovation framework and how it can help you accelerate your digital first strategic plans. With me today is Kevin Coombs, Director of Analytics and Data Science at Rangle. Welcome to the show, Kevin.

00:58

Kevin: Thanks, Jenn, good to be here.

00:59

Jennifer: I was wondering if maybe you could just give me a little bit of background about how you came to this beautiful world of data. One of my favorite topics in the world. And now that you're at Rangle, what is your mandate at Rangle?

01:10

Kevin: Sure, so how I came into it, I started as an analyst at Loyalty One in sort of an entry level position. And basically spent seven years there and learned almost everything about data that I needed to learn. About how it was used, where it comes from, how to unlock its value, how to apply it, some of the tools we would need. I spent some time at a consulting company. And for the last ten years I was at Auto Trader and I was their VP of analytics there. And we took that business from a group of one person looking at analytics, which was me, and grew it up into a size of about 17 people including market research, data, data science, building models, doing web analytics and doing customer centric analytics and segmentation.

01:53

So really grew the business up so that they were able to use their data to unlock value for the business, but also for its client set as well.

01:59

Jennifer: And every marketer out there is going, "Tell me all the things,"

02:02

Kevin: Tell me the all the things. Yeah, the list of things is endless, is literally endless. Every time you turn around you can find something to throw some data at and make a better decision.

02:13

Jennifer: So that brings up something that's really interesting to me is, and I think that we need to start from the beginning, which is I would love to hear from you your definition of data. What is data ... but more importantly is analytics? Because data is one thing, but it's the analytics that make me perk up whenever you and I are having a conversation.

02:33

Kevin: Yeah. I mean there's a lot of different definitions for analytics, but it's essentially, it's the process of taking data and turning it into insights and make a better decision. And so you have, there's three steps. There's the data. So you have to actually have something ... You have to have some data to use. There's the transformation into insights, which is actually a process. It's not a tool or a piece of software or a person, it's an actual process. It's a discipline. But the most important piece is that third step, which is to make a better decision.

03:01

So lots of companies do collect lots of data and do lots of analytics, but then they sit on the shelves and they don't necessarily make a better decision out of it. And so that's the three-step piece. And you have to have all three working together to really unlock a lot of the value out of the data and the analytics practices that businesses might have in their place.

03:18

Jennifer: So let's dig into those three steps a little bit. So first of all, there's the data. Talk to me about what that looks like in terms of ... so lots of people have information flowing in and out, but what is the information, or how should people be looking at data if you know that you need to leverage it via insights as the next step?

03:35

Kevin: So they should really be thinking about the data that's in their business as a strategic asset. In much the same way that they have hardware or people, the data that they generate is probably the most valuable thing that those companies actually end up having. And so they should be putting plans around it. They should be looking for opportunities to apply it to business problems instead of sort of just collecting everything that they can and shoving it into a corner.

04:02

They should be trying to think about ways to find business problems and trying to see not only how can we think about it in terms of solving them for people or for process, but also like what, how, what data might you need to help make this problem go away? To solve this problem, to create some value out of the data that we have so we can make a better decision to make the business grow better.

04:23

Jennifer: So next step then we've got insights, which is something that you're deeply involved with clients. So what does that process, as you alluded to earlier, of insights, of collecting insights or providing insights?

04:34

Kevin: So I think the, the key first step is to actually identify a business problem. So lots of businesses, or some businesses will collect data and they'll try and grab their arms around as much of it as they can. And they'll turn it over to somebody like myself or a data scientist and say, "Just find some trends in this. What do you think this ends up seeing?"

04:57

And those are companies that usually end up spending a lot of time treading water, not getting a ton of value out of their, out of their data. And so having a problem to focus on is the first step in generating some of ... is generating those insights. Otherwise you've got data, it has no home, it has no value. And so being able to find a problem, have it be a prescriptive problem, and then generating, collecting the right data to apply to solve that problem is an important step in generating insights. Because insights is only useful to people when they actually have a business problem. Lots of times I've worked in places where I've received calls from other members of the organization, sales groups say, "Going out to meet a client, can you give me some insights?"

05:45

And you don't know the client. And so my idea of what's insightful versus what your idea of insightful is two very different things. And depending on what the client is, is that maybe completely over their head? Or it may be things that they already know and therefore are not particularly insightful and don't show a company in the best light. And so trying to kind of frame up insights is it's based on the problem that you're trying to solve.

06:12

Jennifer: So, brings me to your next step, which is leveraging the data. So I've often seen what happens a lot these days, which is what people commonly refer to now as analysis paralysis, right? So we have all of these insights and all of this information. How do you take it from insights and information and apply it? How do you get past that paralysis stage?

06:32

Kevin: So there's a couple ... I've seen analysis paralysis. I mean I've worked in places where sometimes the paralysis is driven by the hypothesis, it's kind of a sort of a reverse hypothesis, which is, I know what the answer is, just go find me the analysis that supports that. Those organizations are not really progressive. They're not open to new approaches, new ideas. And a lot of the people that are working in that analytic space tend to sort of just cycle around a lot. And they don't get a lot of value out of their work because they're providing insights, but they're not the insights that I want to hear. And so therefore-

07:09

Jennifer: Or even the real ones.

07:11

Kevin: Or the real ones is that, you know, I have a hypothesis based on some uninformed perspective and I want you to go find data to match it.

07:19

Jennifer: Right.

07:19

Kevin: Which is not not a great use of people.

07:21

Jennifer: And generally to support a budget decision.

07:24

Kevin: Oh, it's to support a budget decision, to support a decision that is good for their business, to support any, any reason of those things. Any number of things, but it's not letting the data do the talking and it's not having an organization kind of be open to that. So that you have analysis paralysis in that way and that you keep cycling trying to find the one magic answer. I've also seen instances where you get into sort of pre analysis paralysis. Where you have an organization that's starting off something where it has a business question and a problem and it ends up spending a tremendous amount of time gathering data, gathering requirements, making, you know, I liken it to sort of selling your house, how you don't let people into your house until you've cleaned it and spic and span and it smells like apple pie and the flowers are great. Like you spend a lot of time preparing it to perfect house.

08:18

Jennifer: Right.

08:18

Kevin: There's a lot of companies that spend a lot of time preparing for the perfect piece of analysis, but no analysis is ever going to be perfect and it's going to take a long time before you get it to that state. And the situation that you're trying to resolve may have already passed you by.

08:33

Jennifer: Someone could hate apple pie.

08:35

Kevin: Somebody could hate apple pie. Or the market could crash. Or whatever situation you're dealing with has now passed you by. And so these sort of being perfect in the way you wanna approach it, that doesn't really work either. Because you're not being agile, you're not being quick, you're not being speedy. And so what I've often tried to do is I've tried to take the problem and trying to squeeze it down and you know, a common saying I say a lot is don't let perfect be the enemy of good. What do we need to do in order to get something done that can help us make the next decision.

09:09

It's not about making the right decision. It's about making the next decision. And if you can get one pass through that cycle of, you know, build and measure and learn, then you can get the ball rolling. And then you can build the next thing, measure the next thing and learn the next thing. These sort of projects, these monolithic analytic projects where they sort of think I'm going to do it one cycle and I'm going to be done, it doesn't work that way. Organizations aren't built like that. You need to be quick out the door. You need to cycle through things. And you need to be working with people that are prepared to make the next decision and not necessarily the only decision. The final decision.

09:47

Jennifer: It's interesting that point you bring up, because in another episode of the podcast we talked very much about the role of dev ops. And it seems to me that this form of looking at data as insights to inform the next step of the process is very much that sort of development ops sort of approach. Which is, okay, here's some information which we collected because we put it out to 10% or 5% of our customers. Got some information back. But now we're leveraging those insights directly to inform the next iteration of this product development, which is, you know ... and that's the beauty part of someone like me who has been, you know, all of this information, now what? And now it's like, well now this, or now this, or now that. Right?

10:28

Kevin: And it dovetails very well with the agile methodology with sort of lean development. There's a whole field of lean analytics about how you marry lean development practices with lean analytic practices. And they're very, they're very similar, which is get some information. Observe, orient, decide, and then decide again.

10:48

Jennifer: Right.

10:49

Kevin: So just get enough information. Make a decision about what's the next thing that you develop in your priority list. How do you groom your backlog? What do I need to AB test? What are the different features? Who might I send this out to? How does my model work? How does my customer care center, for example, what scripts are they potentially using, how do I approach them? What offers do I give them? All of these things are options. And so the analysis supports variants of these.

11:14

Go out and test them, observe, see what happens, feeds back into the process and make the next decision. But you've got a single direction that you're moving towards. And really what you're doing is you're iterating through the different options to get you to that ultimate destination of reducing churn, optimizing your marketing span, reducing your cost per lead, increasing basket size-

11:36

Jennifer: Mitigating risk.

11:37

Kevin: Mitigating risk. Coming to the, you know, optimizing any of these variables. That's your North Star. And what you're doing is you're just trying to inform how do you get to that next decision?

11:46

You know, one of the profs I had in my master's program was very, he said something very wise. He's like, "No model is perfect. It just has be better than the thing you were going to do before." It just has to give you a better outcome than what you were going to do before. It doesn't have to give you the perfect outcome, just something better than it was before. And once you kind of embrace that it nothing, you have no singular decision and it's not going to be one model, then it gives you a little bit more freedom to kind of iterate through and support the rest of your team and the organizations that are doing these sort of agile development practices as well.

12:19

Jennifer: And getting you right back to basics, which is customer centricity.

12:22

Kevin: Yep. Absolutely.

12:23

Jennifer: I mean, and that's the thing that I think that data has done for me or the insights has done for me is to allow me to get back to those basics of customer centricity. And I'm able to have that conversation internally because I say, "Well, it is not my opinion. It is not even my hypothesis. It is straight data directly from the horse's mouth." So to speak, right?

12:41

Kevin: Mm-hmm (affirmative).

12:42

Jennifer: Just powerful.

12:43

Kevin: Absolutely. I mean, the teams I've been working with as I've tried to make sure that they're positioning it as a, as fact ... there's always stories to tell. You know, everything has your own perspective and opinion on it, but it's driven by, it's driven by fact, and you can wrap the narrative around that. As opposed to, like I mentioned before, the other people, you know, other instances where I have an outcome an I just want to warp the data into looking at it in such a way that tells the outcome that I want.

13:13

You don't, you don't do yourself any any favors by trying to torque a story and trying to torque some data. Yeah. You don't do yourself any favors because when it doesn't go well, guess where they're going to come. They're going to come back to you. And so it's in your best interest to make sure that you're understanding all the different options. You know, your points of view, as a client, your point of view, where do you see things, how might you know, how might you react to this? You know, what are the different options that I should be looking at to consider so that when I give you a recommendation and you take that decision forward you feel comfortable with it.

13:45

Jennifer: And how wonderful is that, right? Like if everyone in the organization is using the insights to have a shared understanding of the goals or the customer, like that's where that beauty comes in, the collaboration.

13:55

Kevin: Absolutely. I mean, when I first joined, when I first joined Trader, we joked that we spent 58 minutes of every meeting deciding what, whose numbers were right. And then two minutes deciding with the implication of the numbers were. And obviously we've flipped that on its head. And all the discussions are data centric now and you know, the data team and the insights team is front and center in terms of providing direction and support for the executive.

14:18

But absolutely, there's the easiest conversations to have is that when there's no dispute of the fact it's really about interpretation of the story. And every organization has its own, you know, people and they want to interpret the story.

14:30

Jennifer: All that time and effort and refocus it on well, what will we do with this?

14:35

Kevin: That's the conversation that you want to have. And when you walk out of a, when you walk out of ... everybody's walked out of an hour long meeting feeling exhausted. Like it's been a seven hour meeting. And then everybody's walked out of an hour meeting where they've had good discussion, where they've aired the data, they've had a good robust discussion about it, its implications with some followup questions that might be had, some clear direction and here's the next step. And you come over and I was like, that was half hour massage. It was amazing. I feel invigorated, I'm back to my desk I want to do all of those things again and start this process over.

15:03

Those are the meetings that you want to have more of. You want to have less of the grind meetings. And data helps, helps lubricate those meetings, makes them a lot easier to have.

15:13

Jennifer: Holy velocity, Batman.

15:14

Kevin: Yes, for sure.

15:17

Jennifer: Where are we today in the focus on data? Like if we're gonna think of data as sort of like a bell curve, where are we right now in terms of not just the focus on data but in companies really understanding fundamentally that it's the insights that need to drive decisions. Where would you say we're in in that adoption?

15:31

Kevin: I think if you ask a lot of companies, they'll tell you that they're doing analytics. There'll be some companies that have data science people in analytics departments, but if you really dug down and sort of did those three components of data, insights and action, I think that you would find that there's not as lot of the data is being used, the analysis might be happening, but the action is also a bit waning.

15:58

I think there was a report that I saw from McKinsey that said maybe 30% of all the data that is produced by our organization is actually used in decision making. So 70% of everything that's collected just sits on a shelf and it doesn't even factor into any of the decisions that get made. And there's a ton of data that's being generated every day, but from the business either directly or through what I call digital exhaust, which is the activities that you happen ... you're doing every day and it throws off data about how those activities are working.

16:30

And so I think you've got a lot of organizations that realize that it's important. You know, any Forrester or any of these other companies can do surveys, throw surveys up that say 70% of executives say that analytics and data is an important strategic asset for their business and an important priority. But when you dig down and you look at it, are they using the data that they've got? Some. Are they using all of it? No. Are they organizing themselves to take advantage of that data? Probably not.

16:58

They may be hiring people like myself or other data scientists, but they're probably organizing them in the current setup that they have as just they're putting them into silos. They're working on things that are sort of independent and maybe not cohesively organized. And I think that there's some companies that do really well at this, integrating individuals and data into their operations and they're doing amazing. And then there's other companies that are spending a lot of money, time, effort and resources thinking that they're doing something and they're not getting as far ... they aren't getting as far as they think they should or that they could based on what they're investing.

17:32

Jennifer: And it's always, you know, sort of confusing to me because the risks of bad data management or collecting all of this data and having it just sit there, you know, is ripe for something to happen in this data, right?

17:47

Kevin: Right.

17:47

Jennifer: So you think to yourself if you're to have that risk and you're going to take that on and you're going to say to yourself, okay, no we're going to collect all these things. But then to not use it and to leave 70% of that on the table?

17:57

Kevin: I think ... the risk used to be cost. I remember when I first started the very first sort of capability ... technology we were working with, we had to batch our segments overnight. So you would create a segment in the application and you would hit send and it would run over night. And we would literally roll off weeks of transactions every weekend because we only had a finite amount of space to store. So now obviously it doesn't exist anymore. The cost of capturing and storing data is, you know, fractions of pennies. And so the risk is no longer cost, of going and buying a server or a space or whatever. So now that has sort of, from a business perspective, it's like we'll collect everything. It's like, yeah, that's a good idea, to collect everything, good strategy.

18:44

But the risk is now sort of fooling yourself that you're actually doing these analytics and sort of the risk of analysis paralysis or the risk of opportunity cost. Now that you've collected all of these things, great. How do you make them work? How do you use them? How do you take advantage of them? What is your strategy to actually unlock the value that's in your data and integrate it with the rest of your organizations? How does the data support your corporate objectives? And powering those as opposed to having it sitting in a corner and sort of picking it when you need it. So I think the risks have shifted a bit from cost and sort of hardware, software, you know, hard currencies, into now opportunity costs.

19:27

If you don't use this data, if you don't have a proper data strategy, if you don't have a way to use the data to have a better relationship and experience with your consumers, your competitor probably will. And you know, online and digital native organizations there are built around data. And so it is at the heart of their business. Other organizations and businesses that, you know ... like banks are slowly moving into that transformational space, which is where we're working here. They realize that they need to speed up their data, their data and analytics practices and integration with the rest of the business. Because the other organizations are gonna come, they're gonna eat their lunch, they're gonna take their customers.

20:07

Jennifer: You know, customers these days are also not just going, "I accept," Anymore. Right? So they're actually reading the fine print. Customers are becoming more and more savvy with regards to what they're agreeing with, GDPR being a prime example of that.

20:18

Kevin: Sure.

20:19

Jennifer: So I think it is about that, you know, you can't just collect all the things. It's what are you collecting? How will you use my information? Is it aggregate? Is it tied to ... So having that responsibility or having that deep dive into what you're collecting and how you're leveraging it is no longer sort of like, yeah, it's good business practice. But also now it's a responsibility as part of collecting the data.

20:40

Kevin: Absolutely. It's a responsibility. I think that for better or for worse there's been other organizations that have done poor things with consumers' data, and that's heightened their sense of consumers' sense of awareness in terms of what is being collected about me and how is it being used? But in the same vein, I think consumers are prepared and willing to make that trade off of their information, provided that they get something of value back. That they either get a great experience, customized and relevant, pertinent offers, it makes things faster. So collecting information about consumers and using it to preempt a poor experience, to tailor an experience to them helps them checkout faster.

21:24

Jennifer: Pre-fill a passport.

21:25

Kevin: Helps them go through your experience faster. And so consumers are happy to do that because it means that they spend less time trying sort all their paperwork and get all of their stuff together and they've got a better experience. The other thing it does is it heightens their awareness for what is possible. And so when you go online and, you know, you can get your credit report in two clicks, which is amazing, and then you go to a physical bank and they say, "Please fill this in in triplicate and I have to fax it to head office," You sort of scratch your head and go, how ...really?

21:58

Like that's not how this should work. And so the disconnects between what is possible and what they're experiencing start to move. And that puts additional pressure on companies to say, well, we should really get, we should just really try and understand how do we use the data that we're collecting to make a better consumer experience, a better product, a better journey for our customers. Because now their expectations have been heightened.

22:20

Jennifer: So that's a great segue into ... Because that's where we see the bulk of the disruption is people who see a pain point see data that's relatively available, either like open-sourced or easily collectable within l{e three months. And they're creating products. What is the role of data and insights and innovation? What is that relationship?

22:41

Kevin: I mean there's sort of three things, is that data helps put the focus on value. So as we innovate, as we're doing innovation and recruiting new products, we're creating new services. What is the value of that service to the consumer, but also to the business? And data and insights sits at that space of quantifying value. Either in terms of satisfaction, revenue, frequency, like there's myriad metrics that you can use, but there has to be some focus on an understanding what is the value that we are creating for the different parties that are involved.

23:17

The second thing is that innovation revolves around constant improvement. We've got, you know, this iPhone or any of the products that are out there, they're incrementally getting better. And so in order to understand how to get better, where do we need to focus, data and insights powers that. Where are you today in terms of your current levels of satisfaction and performance and value and understanding? Where do consumers see ... what is beneficial to consumers?

23:43

And as groups go out and they doing innovative things on their existing products and improving them, cycling back and saying, well, did that help our net promoter score, our satisfaction, our retention rate, our sales rates, whatever the metric happens to be? Did that actually work? Because, you know, I've often said that people give birth to products and ideas, but sometimes they do a terrible job at raising them. And so data and analytics, that's there to help them raise their children better, to help them, you know, get them to grow up and not get tattoos and go off to university.

24:17

Jennifer: No offense to tattoos.

24:19

Kevin: No, of course not. My mother said don't get a tattoo and don't get your ear pierced. But yeah, that's what data and analytics has helped to do. Because otherwise where will you go? It's just your guess, right? I think it should be red. Well, why do you think it should be red? Or I think we should sell it-

24:35

Jennifer: Also, nobody cares what you think.

24:36

Kevin: Like, I think we should sell it at a discount. I'm like, okay, well we should test these things and really understand whether or not that's innovative. And the third thing is that, you know, innovation comes from the, again that build measure, learn, lean agile methodology. And you know, you can build. But data and analytics helps the measure part, right? And then the learn is the two parties coming together and saying, this is what I want and this is what I see. Then therefore I've learned this and now that means I need to go back and build something else.

25:02

So there's lots of sort of frameworks, and flywheels, and acronyms and all those pieces. And it doesn't matter really which one you pick as an organization, but probably in the middle is data and analytics. Because you've built something, you've let it off into the world and you need to innovate on it or let it, you know, let it die if it's not working or whatever it happens to be.

25:28

But how are you going to figure that out if you don't collect any data? And if you don't collect any data, how are you going to determine whether or not there's any insight out of this new product. And then if you don't have any insights, how will you know what decisions to make unless you're using your own personal opinion and substituting that for data.

25:44

Jennifer: It's centralizing the data. Like I've seen this over and over and over, you know, we've got POS data, and then we've got online data, and then we've got loyalty member data. And they're looking at these as fundamental silos. And I'm thinking to myself what would happen if you layered those three pieces of data, which I did at a retailer, and all of a sudden there was a white space which nobody knew about, which was being way under serviced in the community where we were able to not only identify but action instantly.

26:11

Kevin: I think a lot of the work in a, the data analytics spaces is not, is going to be about sort of data engineering. Is to take these giant silos of data that sit in different areas of the business and start to combine them in ways that unlock value. I saw a study, it was like 80% of a data scientist's time right now is spent actually finding data and cleaning it so that he can actually do his work. So they, you know, imagine spending four out of five days sitting in Sequel, looking around, sending emails, pulling data together in CSVs and texts and writing scripts. And then Friday now you can do some analysis to answer that big business question that your boss came and asked you on Monday.

26:59

So a lot of the work is in bringing those things together, but trying to figure out how you bring them together. It comes back to the previous point about you have to have a project and a focus. And so if you understand what problem you're trying to solve then where you get data, how you bring it together, how you structure it, what its definitions are, what are the right tools and where do I integrate it, that it becomes very easy.

27:22

When you don't have a problem, but you know that the call center has all the call log data, you know that sales people have all their call sheets, you know that your finance system has all of its transactions and you know that the loyalty programs got tons of skewed data. And somebody comes and is like, "I think we should log all these together." You go, "Well, why?" Like for what purpose? For what value? And you need that sort of catalyst of what's a business problem, what's a use case?

27:47

And so a lot of the companies that I, that we work with, and of struggle with that is let's just sit down and figure out what are all your different use cases? Like where are the things that you've sat down and went, man, if I knew X, I could make this decision. And that's the framework I often use is like, what's a business question that you have, what's the question that you have, and then what decision would you make from that?

28:05

And you can start to pull out, you know, smaller cases ... of small applications of how you can bring data and analytics to the fold and start that catalyst of don't take all the data, take only what you need, bring it together, combine it, solve this problem, demonstrate value, get people's attention. And then start the process kind of over again. Next problem, more data, next problem, more data.

28:29

And what that does is it starts to snowball. As opposed to this dating process of spending six months, seven months, eight months saying, okay, at the end of nine months we will have cleaned up all of our customer call center data. I'm like, that benefits nobody. And also you just cashed a really large check. Like now what have I got for that? And the answer is not a lot. And so approaches like that don't tend to work a lot because people see you working on something for six months. It's not delivering anything. You spent a lot of money. I still have these other business problems. In fact they've probably gotten worse. And so starting with the problem, small problem, collecting pieces of information from where you need to, working with them in an agile way and start snowballing them. That's the best approach to use.

29:11

Jennifer: And I think that that's an interesting point, Kevin, because I think a lot of people are seeing themselves like, what is the root why? And it doesn't have to be this incredibly sophisticated business question that you come up with. It could be as simple as how many customers come to my site on a daily basis and what percentage of those checks out? And what happens in between? Like even if you just started there, or you just started with, you know, what is there ... we have a client, we work with Staples and they knew that they had a problem with people filling out forms to create business cards or whatever.

29:42

And they just started there. And the amount of information that came from them observing and collecting information about that user experience ended up fueling a huge POC in-store. So I think that's really interesting that it is, regardless of how big or wide the question is, getting to that hyper personalization or hyper contextual suggestions, or whatever it is you're looking to solve, it is starting with that, what if we just looked at this?

30:08

If I'm looking at a customer from point A to point B, what is the pain? What's the stop? Even if it's just 10% of your people aren't getting to checkout, why?

30:16

Kevin: Absolutely. I think the smaller you make the problem and the more tactical it becomes, the easier it is to know whether or not you're on the right track. And the easier it is to actually implement some of those things. Because if the problem is too abstract or it has multiple people who are interested in solving it, or want their two bits into it, have their say, it becomes hard to kind of execute. And hard to can kind of conceptualize and push out the door.

30:42

So the smaller you can make it the easier it is because you can cycle through it faster, you can generate more value and that's the key to kind of getting the ball rolling on a lot of these initiatives.

30:54

Jennifer: If I asked you five years ago, and then asked you again today how data is changing the way business operates, same answer or different answer?

31:06

Kevin: Probably a different answer. I think five years ago data was all about structured data, so transactions and loyalties, skew and things like that. It was about organizing that information and unlocking some of its value. I think today it's less about that. It's more about all of the data and being focused ... like, so unstructured data, so things like text, voice, all of that is now sort of up for grabs. Think about, you know, I'm an older guy, think about Twitter like five years was Twitter as important as it was today? It was important but probably not as important in terms of moving brands.

31:54

Jennifer: Then considering the president of the United States uses it as his primary communication platform. It's interesting, right?

32:00

Kevin: It's interesting, although I don't want to know what the analysis of his texts is going to look like. But, but like, the power of social media five years ago was important, was a consideration, I would say in some brands. And some consumers' experiences. And now there's entire businesses that are made up entirely ... like all of just social media. Like it is their bread and butter.

32:23

And so that kind of data, it gets, it's exponentially complexified, the amount of data that's available. And so I think right now people are struggling with trying to pull all the arms, pull all of these things together. And I think five years from now you'll probably have technological solutions that will solve a lot of those things. And so that process of data into insight to make better decisions, right now their data is a ton of data. The insights piece is largely manual, but there's people there. And then decisions is a lot of people.

32:58

I think in five years the data is probably going to be even bigger, which is going to make the insights and the decisions piece lot more automated. So you're going to get a lot more artificial intelligence and machine learning and recommendations. And those will be the things that will start to make some of the decisions and sort of simplify a lot of the connections.

33:23

People will still be needed to sort of point the technology in the right direction and frame it up. Because if you ask a bad question you're going to get, you know, an irrelevant answer. And you know, Jan who works here, he sort of tells a funny story about you know, letting loose some machine learning on the poor data and you know, getting recommendations that don't make any sense at all.

33:45

And so technology is agnostic. It doesn't know what it doesn't know. And so you let it loose on some poorly structured data with a poorly defined question and you're going to get a pretty interesting answer. So you still need people to guide, and tailor and understand.

34:00

Jennifer: We're curious.

34:01

Kevin: And we're thinking about ways that this answer is going to help them move business. And so people like myself that are a little bit more ... a lot more on the business side and the technology side, help frame those questions up so that the technology that gets built makes sense and plugs, you know, perfectly into the business.

34:21

So the technological solution plugs into the business, answers the question and you maximize the value from your technology. You have a technology sitting all by itself creating things for its own interest, you may not have the connection back to the business, which means you don't get as much value.

34:36

And so in the future the technology is going to be more important, but it's going to shift the focus on making sure that you have the right business question. And the right frame for that technology.

34:46

Jennifer: Right. I love the idea that it's, you know, the force to customer centricity and then being focused on customer centricity means you're asking the right questions and leveraging the data. That feels like a really great feedback loop that I'm looking forward to getting to.

35:00

Kevin: Yeah, if you don't ask the right questions, like a lot of the challenges that we have, a lot of challenges, you know, that I've experienced is that the questions are poor. Poorly worded questions are one of the biggest time sinks. You don't think about what the right question is, and therefore any answer, the answer that comes back is probably not the right one.

35:19

And so we spend a lot of time helping businesses think about the right questions and the ways to apply the technology before we actually roll up our sleeves and get in it. Because if you don't have the right questions, you're just going to spin your wheels.

35:34

Jennifer: It's exciting times.

35:35

Kevin: There's a lot of work to be done. And it's an interesting field and it's one I've been in, stuck with for a while. So it's always changing and there's always lots of opportunities.

35:43

Jennifer: It's a good time to be curious.

35:44

Kevin: It is a very good time to be curious. There's a lot of, there's a lot of demand.

35:50

Jennifer: Thank you so much for listening to What's Your (R)angle. We hope that you found some moments that made you think and hopefully spark some questions and/or conversations at your organization. Follow us on iTunes and Spotify to get notifications for all future episodes, and you can now watch our podcast on YouTube, so be sure to like and subscribe.

36:09

We'd love to hear your thoughts and your questions, what your Rangle is, so please reach out to us on Twitter by using the hashtag #askrangle, or email us podcast@rangle.io. Many thanks to you Kevin, our Director of Analytics and Insights. We're so glad to have you and really glad to have you on the show. Thanks for taking the time to speak with us today.

36:29

Kevin: No problem. Thanks.

36:30

Jennifer: Thanks for joining us and please do reach out. We thank you for watching. Have a fantastic day.

36:35

Thanks for listening to What's Your (R)angle? We hope you found some moments that made you think, taught you something new or sparked a conversation. Follow us on iTunes and Spotify to get notifications for all future episodes. We'd love to hear your thoughts on what we discuss each week and hear your point of view on the topics. So be sure to let us know what your Rangle is, or using the hashtag #askrangle on Twitter or by emailing us at podcast@rangle.io.